1. Field
Example embodiments of the following description relate to an apparatus and method for calculating a cumulative histogram of an image, and more particularly, to a technology that may reduce hardware costs by applying a scheme of calculating a cumulative value obtained by accumulating data associated with a number of combinations of the input data and of selectively loading the calculated cumulative value in a corresponding bin of a histogram.
2. Description of the Related Art
A histogram is frequently used in an image processing field, and may be generated based on a predetermined reference in a part of or all of areas of an image.
In this instance, the predetermined reference refers to dividing a part of or all of the areas of the image into a finite number of bins, and corresponds to an x-axis of the histogram of the image. A value obtained by adding, to a corresponding bin, information indicated by pixels in an area of the image that generate the histogram refers to a value of a y-axis.
The above histogram may also be used to generate a feature point based on the image and to represent information on feature points neighboring the generated feature point, namely a descriptor.
For example, a scale-invariant feature transform (SIFT) algorithm is widely used in the image processing field, to match feature points, and may provide stable processing results. The SIFT algorithm may generate a descriptor of a feature point, and the descriptor may be represented using a histogram for a gradient of pixels in an area neighboring the feature point.
A histogram in the image processing field is widely used in real-time color correction, tone-mapping, and the like, and is representative of schemes of expressing a characteristic using a distribution of image information.
The above histogram may be generated and processed for all areas of an image, however, may be generated only for a predetermined area of the image.
A feature-based image matching scheme may be used in various image processing fields, for example, motion tracking, detection and recognition of an object or a face, restoration of three-dimensional (3D) space, synchronization of stereo images, and the like.
The feature-based image matching scheme may enable matching of feature points using at least two images, and may use the histogram using a descriptor representing properties of feature points.
To use the SIFT algorithm in high-speed processing of a high-quality, high-capacity image, processing using hardware may be required due to complexity of the SIFT algorithm. In this instance, when a histogram of a predetermined area of an image, for example a descriptor, is generated, when the histogram is implemented using hardware, an adder may be used as a structure allocated to a predefined bin, based on a size of an area.